Production of Se-methylselenocysteine in transgenic plants expressing selenocysteine methyltransferase
Bibliographic record
Abstract
BACKGROUND: It has become increasingly evident that dietary Se plays a significant role in reducing the incidence of lung, colorectal and prostate cancer in humans. Different forms of Se vary in their chemopreventative efficacy, with Se-methylselenocysteine being one of the most potent. Interestingly, the Se accumulating plant Astragalus bisulcatus (Two-grooved poison vetch) contains up to 0.6% of its shoot dry weight as Se-methylselenocysteine. The ability of this Se accumulator to biosynthesize Se-methylselenocysteine provides a critical metabolic shunt that prevents selenocysteine and selenomethionine from entering the protein biosynthetic machinery. Such a metabolic shunt has been proposed to be vital for Se tolerance in A. bisulcatus. Utilization of this mechanism in other plants may provide a possible avenue for the genetic engineering of Se tolerance in plants ideally suited for the phytoremediation of Se contaminated land. Here, we describe the overexpression of a selenocysteine methyltransferase from A. bisulcatus to engineer Se-methylselenocysteine metabolism in the Se non-accumulator Arabidopsis thaliana (Thale cress). RESULTS: By over producing the A. bisulcatus enzyme selenocysteine methyltransferase in A. thaliana, we have introduced a novel biosynthetic ability that allows the non-accumulator to accumulate Se-methylselenocysteine and gamma-glutamylmethylselenocysteine in shoots. The biosynthesis of Se-methylselenocysteine in A. thaliana also confers significantly increased selenite tolerance and foliar Se accumulation. CONCLUSION: These results demonstrate the feasibility of developing transgenic plant-based production of Se-methylselenocysteine, as well as bioengineering selenite resistance in plants. Selenite resistance is the first step in engineering plants that are resistant to selenate, the predominant form of Se in the environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".